In a variety of fields and situations, it is useful to draw a conclusion based on a set of empirical data. There are many situations in science, engineering, medicine, and other fields where it is desirable to conclude which of a set of possible conditions or states exist, or predict which of a set of possible events will occur in the future. For example, it may be advantageous to be able to analyze a set of data from one or more patients in order to diagnose whether any of them has a particular disease, or to analyze data to determine which patients are likely to develop a disease in the future.
Conventional predictive methods may not be adequate to accurately make predictions in all cases. For example, in the field of medicine, research often fails to make any statements about any particular patient, instead generating conclusions about the prognostic factors generalizable to some pre-specified target population of patients.
Furthermore, conventional research conclusions based on isolated or otherwise limited samples (e.g., on patients drawn from a single institution) are frequently criticized as “biased”, “unrepresentative”, or “not population-based” because they are believed not to be representative of a broader patient population. Additionally, conventional research conclusions are often presented as attributes of the prognostic factors rather than as attributes of individual patients. That is, they are often displayed and organized factor-by-factor, not patient-by-patient, and describe the factors themselves, but say nothing about individual patients.
Embodiments of the invention address these and other issues.